Design and Adjustment of Optimizing Athletes' Training Programs Using Machine Learning Algorithms DOI Creative Commons
Li Zhang

Deleted Journal, Journal Year: 2024, Volume and Issue: 20(6s), P. 2014 - 2024

Published: April 29, 2024

The adjustment of optimizing athletes' training programs using machine learning involves leveraging data-driven approaches to enhance regimens and performance outcomes for athletes. By analyzing various factors such as physiological data, logs, metrics, external conditions, algorithms can identify patterns, correlations, optimal strategies. These insights enable coaches sports scientists tailor more effectively individual needs, goals, abilities. continuously adapting refining plans based on real-time feedback data analysis, helps optimize preparation, recovery, overall performance, ultimately maximizing their potential success in competitive sports. This paper explores novel methodologies machine-learning techniques aimed at programs. With the increasing demand peak injury prevention sports, there is a growing need effectively. One methodology, Optimized Adjustment Evolutionary Computing Feature Selection (OA-EC-FS), investigated its ability select relevant features crucial enhancing across disciplines. Additionally, are employed classify selected features, enabling trainers make informed decisions maximize outcomes.

Language: Английский

Design of Training Load Monitoring and Adjustment Algorithm for Athletes: Based on Heart Rate Variability and Body Index Data DOI Creative Commons
Ke Zhou

Deleted Journal, Journal Year: 2024, Volume and Issue: 20(6s), P. 1600 - 1611

Published: April 29, 2024

The training load monitoring and adjustment algorithm for athletes, based on heart rate variability (HRV) body index data, offers a comprehensive approach to optimizing athletic performance minimizing the risk of injury. By leveraging HRV which reflects autonomic nervous system's response stress, measurements such as mass (BMI) or fat percentage, provides insights into athletes' physiological readiness recovery status. design an effective is critical while injury overtraining. This paper proposes novel that integrates data tailor programs individual athlete needs. presents innovative utilizing data. Through continuous analysis metrics RMSSD LF/HF Ratio, in conjunction with personalized management strategies are developed optimize mitigating optimization outlined this study allows real-time adjustments loads responses, ensuring athletes receive tailored maximize gains promote long-term health well-being. coaches sports scientists can enhance outcomes support overall development longevity careers.

Language: Английский

Citations

1
Design and Adjustment of Optimizing Athletes' Training Programs Using Machine Learning Algorithms DOI Creative Commons
Li Zhang

Deleted Journal, Journal Year: 2024, Volume and Issue: 20(6s), P. 2014 - 2024

Published: April 29, 2024

The adjustment of optimizing athletes' training programs using machine learning involves leveraging data-driven approaches to enhance regimens and performance outcomes for athletes. By analyzing various factors such as physiological data, logs, metrics, external conditions, algorithms can identify patterns, correlations, optimal strategies. These insights enable coaches sports scientists tailor more effectively individual needs, goals, abilities. continuously adapting refining plans based on real-time feedback data analysis, helps optimize preparation, recovery, overall performance, ultimately maximizing their potential success in competitive sports. This paper explores novel methodologies machine-learning techniques aimed at programs. With the increasing demand peak injury prevention sports, there is a growing need effectively. One methodology, Optimized Adjustment Evolutionary Computing Feature Selection (OA-EC-FS), investigated its ability select relevant features crucial enhancing across disciplines. Additionally, are employed classify selected features, enabling trainers make informed decisions maximize outcomes.

Language: Английский

Citations

1